{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import copy\n",
    "import glob \n",
    "import json\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import logging\n",
    "sns.set() # clear whole style\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check passed!\n"
     ]
    }
   ],
   "source": [
    "def make_col_readable(col_data, raw_key):\n",
    "    \"\"\"Make single column data readable.\"\"\"\n",
    "    k,m,g = 1024, 1024*1024, 1024*1024*1024\n",
    "    \n",
    "    col_mean = col_data.mean()\n",
    "    \n",
    "    if g <= col_mean:\n",
    "        new_col = col_data / g\n",
    "        new_key = \"{}({})\".format(raw_key, \"G\")\n",
    "    \n",
    "    if m <= col_mean < g:\n",
    "        new_col = col_data / m\n",
    "        new_key = \"{}({})\".format(raw_key, \"M\")\n",
    "        \n",
    "    elif k <= col_mean < m:\n",
    "        new_col = col_data / k\n",
    "        new_key = \"{}({})\".format(raw_key, \"K\")\n",
    "    else:\n",
    "        new_col, new_key = None, None\n",
    "    return new_col, new_key \n",
    "\n",
    "def make_human_readable(data, keys):\n",
    "    \"\"\"Make a pandas data readable.\"\"\"\n",
    "    updated_map = dict()\n",
    "    if not isinstance(keys, list):\n",
    "        keys = [keys]\n",
    "    for k in keys:\n",
    "        col_data = data[k]\n",
    "        updated_val, updated_key = make_col_readable(col_data, k)\n",
    "        if not updated_key:  # \n",
    "            continue \n",
    "            \n",
    "        # add new column\n",
    "        data[updated_key] = updated_val\n",
    "        updated_map.update({k: updated_key})\n",
    "    \n",
    "    return data, updated_map\n",
    "\n",
    "\n",
    "METRIC_MAP = {\n",
    "    \"Classification\": \"accuarcy\",\n",
    "    \"Super-Resolution\": {\"psnr\", \"ssim\"},  # and \n",
    "    \"Segmentation\": \"mIOU\",\n",
    "    \"Detection\": {\"F1 score\",  \"LAMR\"},  # or \n",
    "    \"Click-Through Rate Prediction\": \"AUC\",\n",
    "}\n",
    "\n",
    "PARAMS_SET = set([\"model_size\", \"metric\", \"params\", \"flops\", \"Inference Time\"])\n",
    "\n",
    "def get_params_set(case_type, default_params, metric_map):\n",
    "    \"\"\"Get params set.\"\"\"\n",
    "    ret_params = copy.deepcopy(default_params)\n",
    "    if \"metric\" in default_params:\n",
    "        ret_params.remove(\"metric\")\n",
    "    metric_keys = metric_map.get(case_type)\n",
    "    if not metric_keys:\n",
    "        raise KeyError(\"not found case: {} in map: {}.\".format(case_type, metric_map))\n",
    "    elif isinstance(metric_keys, str):\n",
    "        ret_params.add(metric_keys)\n",
    "    elif isinstance(metric_keys, set):\n",
    "        ret_params |= metric_keys\n",
    "    else:\n",
    "        raise ValueError(\"unkown value: {}\".format(metric_keys))\n",
    "        \n",
    "    return ret_params\n",
    "\n",
    "def test_get_params():\n",
    "    detec_target = {'params', 'F1 score', 'model_size', 'Inference Time', 'flops', 'LAMR'}\n",
    "    assert get_params_set(\"Detection\", PARAMS_SET, METRIC_MAP) == detec_target\n",
    "    \n",
    "    classif_target = {'params', 'accuarcy', 'model_size', 'Inference Time', 'flops'}\n",
    "    assert get_params_set(\"Classification\", PARAMS_SET, METRIC_MAP) == classif_target\n",
    "    print(\"check passed!\")\n",
    "    \n",
    "if __name__ == \"__main__\":\n",
    "    test_get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _get_json_val(json_file):\n",
    "    \"\"\"Get json value.\"\"\"\n",
    "    with open(json_file, \"r\") as json_obj:\n",
    "        single_val = json.load(json_obj)\n",
    "    return single_val\n",
    "\n",
    "def gather_metrics_from_dir(dir_path, case_type):\n",
    "    \"\"\"Collect the results under path.\"\"\"\n",
    "    json_files = glob.glob(os.path.join(dir_path, \"*.json\"))\n",
    "    need_keys = get_params_set(case_type, PARAMS_SET, METRIC_MAP) \n",
    "    # print(\"json_files: \",json_files)\n",
    "    ret_val_total = list()\n",
    "    \n",
    "    # try first json with keys check\n",
    "    single_val = _get_json_val(json_files[0])\n",
    "    unused_key = set([_k for _k in need_keys if _k not in single_val])\n",
    "    need_keys -= unused_key\n",
    "        \n",
    "    # move set to list, own to order\n",
    "    need_keys = list(need_keys)\n",
    "    for _json_file in json_files:\n",
    "        json_value = _get_json_val(_json_file)\n",
    "        single_val = list([float(json_value[k]) for k in need_keys])\n",
    "        \n",
    "        # add json_file_name, consider the type between json file and data \n",
    "        single_val.append(_json_file)\n",
    "        ret_val_total.append(single_val)\n",
    "        \n",
    "    ret_np = np.array(ret_val_total)\n",
    "    \n",
    "    ret_pd = pd.DataFrame(ret_np, columns=[*need_keys, \"json_name\"])\n",
    "    ret_pd[need_keys] = ret_pd[need_keys].apply(pd.to_numeric)\n",
    "    ret_pd.sort_values(\"model_size\", inplace=True)\n",
    "    return ret_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_pruning_case(pdf, x_label, y_label, to_png_file=\"defaut.png\", dpi=300):\n",
    "    sns.set()\n",
    "    sns.set(rc={\"figure.figsize\": (6.4, 3.6), \"figure.dpi\": 300})\n",
    "    # sns.set_style(\"white\")\n",
    "    # sns.set_context(\"paper\", font_scale=1.5, rc={\"lines.linewidth\":0.5})\n",
    "    sns.set_style(\"dark\")\n",
    "\n",
    "    fig,axes = plt.subplots(nrows=2,ncols=2,figsize=(6.4,3.6))\n",
    "    fig.subplots_adjust(hspace=0.1, wspace=0.25)\n",
    "    plt.ticklabel_format(style='plain', axis='y')\n",
    "\n",
    "    for count, (x, y, ax) in enumerate(zip(x_label, y_label, axes.reshape(-1))):  \n",
    "        ax= sns.scatterplot(x=x, y=y,data=pdf,ax=ax)\n",
    "        ax.set_xlabel(x,fontsize=6)\n",
    "        ax.set_ylabel(y,fontsize=6)\n",
    "\n",
    "        ax.tick_params(axis='y',labelsize=4, rotation=0)\n",
    "        ax.tick_params(axis='x',labelsize=4, rotation=45)\n",
    "        # ax.ticklabel_format(style='plain', axis='y',useOffset=False)\n",
    "\n",
    "        if count < 2:\n",
    "            ax.set(xlabel=None)\n",
    "            ax.set(xticklabels=[])\n",
    "\n",
    "    fig.savefig(to_png_file, dpi=dpi, bbox_inches='tight')\n",
    "    return to_png_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameter description\n",
    "\n",
    "- model_size  (y)  for y axis\t\n",
    "- accuracy\t (y)  metric types，support defined by user\n",
    "- flops\t(y) \n",
    "- params (y)    \t\n",
    "- latency_batch \t(y)   batch = (1, 32), batch 32 defautl\n",
    "- latency_batch(1)      # use `Inference Time = latency_batch/batch`   \n",
    "- latency_batch(32)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collect the json file under special path\n",
    "\n",
    "**Input**\n",
    "- dir_path: Directory to read json file\n",
    "- case_type: case to analysis, choose from the keys of METRIC_MAP\n",
    "\n",
    "**Output**：\n",
    "- pandas.DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Inference Time</th>\n",
       "      <th>accuarcy</th>\n",
       "      <th>model_size</th>\n",
       "      <th>flops</th>\n",
       "      <th>params</th>\n",
       "      <th>json_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.28</td>\n",
       "      <td>95.923477</td>\n",
       "      <td>7.72</td>\n",
       "      <td>469.0</td>\n",
       "      <td>1.878</td>\n",
       "      <td>./raw_metric1014/performance_104.json</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.00</td>\n",
       "      <td>96.584535</td>\n",
       "      <td>8.45</td>\n",
       "      <td>548.0</td>\n",
       "      <td>2.173</td>\n",
       "      <td>./raw_metric1014/performance_48.json</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.62</td>\n",
       "      <td>96.744791</td>\n",
       "      <td>9.32</td>\n",
       "      <td>620.0</td>\n",
       "      <td>2.389</td>\n",
       "      <td>./raw_metric1014/performance_69.json</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82.72</td>\n",
       "      <td>97.055288</td>\n",
       "      <td>10.50</td>\n",
       "      <td>729.0</td>\n",
       "      <td>2.701</td>\n",
       "      <td>./raw_metric1014/performance_42.json</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88.98</td>\n",
       "      <td>97.245592</td>\n",
       "      <td>11.30</td>\n",
       "      <td>786.0</td>\n",
       "      <td>2.884</td>\n",
       "      <td>./raw_metric1014/performance_52.json</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Inference Time   accuarcy  model_size  flops  params  \\\n",
       "0           51.28  95.923477        7.72  469.0   1.878   \n",
       "4           69.00  96.584535        8.45  548.0   2.173   \n",
       "3           71.62  96.744791        9.32  620.0   2.389   \n",
       "1           82.72  97.055288       10.50  729.0   2.701   \n",
       "2           88.98  97.245592       11.30  786.0   2.884   \n",
       "\n",
       "                               json_name  \n",
       "0  ./raw_metric1014/performance_104.json  \n",
       "4   ./raw_metric1014/performance_48.json  \n",
       "3   ./raw_metric1014/performance_69.json  \n",
       "1   ./raw_metric1014/performance_42.json  \n",
       "2   ./raw_metric1014/performance_52.json  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example data typo, accuracy  , not accuarcy!!\n",
    "ret_data = gather_metrics_from_dir(dir_path=\"./raw_metric1014\",case_type=\"Classification\")\n",
    "ret_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ret_data[\"Inference Time\"][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check single json file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model_size': 7.72,\n",
       " 'accuarcy': 95.923477,\n",
       " 'params': 1.878,\n",
       " 'flops': 469,\n",
       " 'Inference Time': 51.28}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('./raw_metric1014/performance_104.json', \"r\") as f:\n",
    "    v = json.load(f)\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show the Pruning case\n",
    "\n",
    "Input：\n",
    "- data with pandas.DataFrame\n",
    "- four label of x axis\n",
    "- for label of y axis\n",
    "- the name of image file to save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./default_purning.png'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1920x1080 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pruning_case(ret_data, x_label=[\"model_size\"]*4,\n",
    "                  y_label=[\"accuarcy\", \"params\", \"Inference Time\", \"flops\"], \n",
    "                  to_png_file=\"./default_purning.png\", dpi=100)  # json key error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['index', 'model_size', 'accuracy', 'latency_per_input', 'flops',\n",
      "       'params', 'latency_sum', 'input_num', 'model_file'],\n",
      "      dtype='object')\n",
      "{'flops': 'flops(M)', 'model_size': 'model_size(K)', 'params': 'params(K)', 'input_num': 'input_num(K)'}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1920x1080 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_from_csv_summarize(csv_file, to_png_file=\"./default_summarize.png\", dpi=300):\n",
    "    \"\"\"Plot pruning png with csv_summarize file.\"\"\"\n",
    "    pdf = pd.read_csv(csv_file)\n",
    "    print(pdf.keys())\n",
    "    \n",
    "    pdf_readable, up_map = make_human_readable(pdf, [\"flops\", \"model_size\", \"params\", \"input_num\"])\n",
    "    print(up_map)\n",
    "    # print(pdf_readable)\n",
    "\n",
    "    plot_pruning_case(pdf_readable, x_label=[\"model_size(K)\"]*4, \n",
    "                      y_label=[\"accuracy\", \"params(K)\", \"latency_sum\", \"flops(M)\"], \n",
    "                      to_png_file=to_png_file, dpi=dpi)\n",
    "    \n",
    "plot_from_csv_summarize(csv_file=\"./resnet20_prune_ea_gpu_latency_batchsize_1.csv\", dpi=100)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
